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Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data

文献类型：期刊论文

作者Wang, Ziyu2,3,4; Cao, Shisong2,5; Du, Mingyi2,5; Song, Wen2,5; Quan, Jinling6; Lv, Yang1,7
刊名REMOTE SENSING
出版日期2023-05-16
卷号15期号:10页码:32
关键词local climate zone multispectral instrument synthetic aperture radar night-time light random forest classifiers
DOI10.3390/rs15102599
通讯作者Cao, Shisong(caoshisong@bucea.edu.cn)
英文摘要Accurate, rapid, and automatic local climate zone (LCZ) mapping is essential for urban climatology and studies in terms of urban heat islands. Remotely sensed imageries incorporated with machine learning algorithms are widely utilized in LCZ labeling. Nevertheless, large-scale LCZ mapping is still challenging due to the complex vertical structure of underlying urban surfaces. This study proposed a new method of LCZ labeling that uses a random forest classifier and multi-source remotely sensed data, including Sentinel 1A Synthetic Aperture Radar (SAR), Sentinel 2 Multispectral Instrument, and Luojia1-01 night-time light data. In particular, leaf-on and -off imageries and surface thermal dynamics were utilized to enhance LCZ labeling. Additionally, we systematically evaluated how daytime and night-time features influence the performance of the classification procedure. Upon examination, the results for Beijing, China, were confirmed to be robust and refined; the Overall Accuracy (OA) value of the proposed method was 88.86%. The accuracy of LCZs 1-9 was considerably increased when using the land surface temperature feature. Among these, the Producer Accuracy (PA) value of LCZ 3 (compact low-rise) significantly increased by 16.10%. Notably, it was found that NTL largely contributed to the classification concerning LCZ 3 (compact low-rise) and LCZ A/B (dense trees). The performance of integrating leaf-on and -off imageries for LCZ labeling was better than merely uses of leaf-on or -off imageries (the OA value increased by 4.75% compared with the single use of leaf-on imagery and by 3.62% with that of leaf-off imagery). Future studies that use social media big data and Very-High-Resolution imageries are required for LCZ mapping. This study shows that combining multispectral, SAR, and night-time light data can improve the performance of the random forest classifier in general, as these data sources capture significant information about surface roughness, surface thermal feature, and night-time features. Moreover, it is found that incorporating both leaf-on and leaf-off remotely sensed imageries can improve LCZ mapping.
WOS关键词URBAN HEAT-ISLAND ; CARBON EMISSIONS ; RANDOM FOREST ; LAND ; CHINA ; WUDAPT ; ENVIRONMENT ; VALIDATION ; SENTINEL-1 ; MOISTURE
资助项目National Natural Science Foundation (NSFC) of China[41930650] ; Scientific Research Project of Beijing Municipal Education Commission - Beijing Key Laboratory of Urban Spatial Information Engineering[KM202110016004] ; Scientific Research Project of Beijing Municipal Education Commission - Beijing Key Laboratory of Urban Spatial Information Engineering[20220111] ; State Key Laboratory of Geo-Information Engineering ; Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM[20020405]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者MDPI
WOS记录号WOS:000996657200001
资助机构National Natural Science Foundation (NSFC) of China ; Scientific Research Project of Beijing Municipal Education Commission - Beijing Key Laboratory of Urban Spatial Information Engineering ; State Key Laboratory of Geo-Information Engineering ; Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM
源URL[http://ir.igsnrr.ac.cn/handle/311030/197568]  
专题中国科学院地理科学与资源研究所
通讯作者Cao, Shisong
作者单位1.Beijing Key Lab Urban Spatial Informat Engn, Beijing 100038, Peoples R China
2.Beijing Univ Civil Engn & Architecture, Sch Geomat & Urban Spatial Informat, Beijing 100044, Peoples R China
3.CASM, State Key Lab Geoinformat Engn, Beijing 100036, Peoples R China
4.CASM, Key Lab Surveying & Mapping Sci & Geospatial Infor, Beijing 100036, Peoples R China
5.Minist Nat Resources, Key Lab Urban Spatial Informat, Beijing 100044, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
7.Beijing Inst Surveying & Mapping, Beijing 100038, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Ziyu,Cao, Shisong,Du, Mingyi,et al. Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data[J]. REMOTE SENSING,2023,15(10):32.
APA Wang, Ziyu,Cao, Shisong,Du, Mingyi,Song, Wen,Quan, Jinling,&Lv, Yang.(2023).Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data.REMOTE SENSING,15(10),32.
MLA Wang, Ziyu,et al."Local Climate Zone Classification by Seasonal and Diurnal Satellite Observations: An Integration of Daytime Thermal Infrared Multispectral Imageries and High-Resolution Night-Time Light Data".REMOTE SENSING 15.10(2023):32.

入库方式： OAI收割

来源：地理科学与资源研究所

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